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Efficient Computation of Portfolio Credit Risk with Chain Default

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  • Ikeda, Yuki

Abstract

Many banks consider the chain default or bankruptcy when they compute the credit loss distribution. One way to consider the chain default is the good-old Monte Carlo simulation, however, it is typically time-consuming. In this paper, we extend the efficient Monte Carlo simulation using the importance sampling introduced by Glasserman and Li (2005) to realize an efficient Monte Carlo simulation of the Value at Risk (VaR) that allows the chain defaults. In addition, we see that another method, the saddle point approximation, can also be modified for the case of the chain defaults. Moreover, we give a simple method of shifting the means of the multivariate factors using the well-known EM-algorithm to further reduce the variance of the simulated VaR. Simulation studies show that these proposed methods have superior numerical performance.

Suggested Citation

  • Ikeda, Yuki, 2021. "Efficient Computation of Portfolio Credit Risk with Chain Default," MPRA Paper 106652, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:106652
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    References listed on IDEAS

    as
    1. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    2. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
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    More about this item

    Keywords

    Value-at-risk; Risk contributions; Importance sampling; Saddle point approximation; EM-algorithm;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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